TensorFlow implementation of Deep Reinforcement Learning papers

A TensorFlow implementation of Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures.

🐋 Simple implementations of various popular Deep Reinforcement Learning algorithms using TensorFlow2

Master classic RL, deep RL, distributional RL, inverse RL, and more using OpenAI Gym and TensorFlow with extensive Math

Informal IPython experiments and tutorials. TensorFlow, machine learning/deep learning/RL, NLP applications.

tensorflow deep RL for driving a rover around

tensorflow deep RL hacking on minecraft with malmo

This repository includes ports of the algorithms from Spinning Up in Deep RL to TensorFlow v2

Implementations of various RL and Deep RL algorithms in TensorFlow, PyTorch and Keras.

Reinforcement Learning (RL), allows you to develop smart, quick and self-learning systems in your business surroundings. It is an effective method to train your learning agents and solve a variety of problems in Artificial Intelligence—from games, self-driving cars and robots to enterprise applications that range from datacenter energy saving (cooling data centers) to smart warehousing solutions. The book covers the major advancements and successes achieved in deep reinforcement learning by synergizing deep neural network architectures with reinforcement learning. The book also introduces readers to the concept of Reinforcement Learning, its advantages and why it’s gaining so much popularity. The book also discusses on MDPs, Monte Carlo tree searches, dynamic programming such as policy and value iteration, temporal difference learning such as Q-learning and SARSA. You will use TensorFlow and OpenAI Gym to build simple neural network models that learn from their own actions. You will also see how reinforcement learning algorithms play a role in games, image processing and NLP.

TensorFlow implementation of Deep RL (Reinforcement Learning) papers based on deep Q-learning (DQN)

A Deep Q-Network based RL solution, namely IoTWarden, developed using TensorFlow, OpenAI Gym, and Python. Simulated a vulnerable IoT environment using Gym, where a defense agent optimally takes actions to block attack activities in real-time. Trained the RL model for 250 episodes, each containing 100 epochs.

Contains implementations of various deep RL algorithms and papers including action conditional video prediction | Python | Tensorflow | Open AI gym

A repository of Q-learning based Deep Reinforcement learning algorithms, including Linear DQN, DQN with experience reply, Dueling DQN and Double Dueling DQN. Mostly tested on Gym environments.

Tensorflow implementation of Deep Reinforcement learning algorithms: Parameter Space Noise for Exploration

My tensorflow 2.0 implementations of RL and DeepRL exercises from Udacity Deep RL Nanodegree

TensorFlow implementation of Deep Reinforcement Learning papers

Basic deep RL in Tensorflow

my implementations of bandits and deep RL models using tensorflow

This repo is not maintained anymore. Some algorithms for solving deep RL problems. Using TensorFlow.

Implementation of a drone control in dynamically changing wind environment using deep RL, Human and Hardware based RL control using using Python and Tensorflow framework

Implementation of autonomous drone control in dynamically changing wind environment using synaptic resistor circuits, human and deep RL using Python and Tensorflow framework

Deep Q-Network (DQN) implementation for solving the CartPole-v1 environment using Reinforcement Learning. The agent learns to balance a pole on a cart through trial and error. Includes TensorFlow-based DQN model, reward visualization, and a real-time simulation with "human-render" mode. Perfect for learning RL basics.

The project focuses on Reinforcement Learning (RL) techniques to solve Real-Time Strategy (RTS) tasks involving training an agent to play video games with continuous gameplay and high-level macro- strategic goals such as map control, economic superiority and more. The model is a convolutional neural network, trained with a variant of Q-learning,whose input is raw pixels and whose output is a value function estimating future rewards.The project started with gathering of theoretical knowledge for understanding Deep Q-Networks and the implementation of the game using Python Libraries including Pygame, Python bindings, Tensorflow. Leanring is shown through visualization through grpahs using Tensorboard.

tensorflow for deep RL

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TensorFlow implementation of Deep Reinforcement Learning papers

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Deep Reinforcement Learning algorithms using TensorFlow 2 and Keras

copy from https://github.com/carpedm20/deep-rl-tensorflow